Analysis of Agriculture and Food Supply Chain through Blockchain and IoT with Light Weight Cluster Head.

By 2050, the world's population will have increased by 34%, to more than 9 billion people, needing a 70% increase in food production. Prepare more dishes with fewer ingredients. Therefore, the critical goal of manufacturers is to increase production while being ecologically benign. Supply chain systems that do not enable direct farmer-to-consumer connection and rising input costs influence data collection, security, and sharing. Constraints on data security, manipulation, and single-point failure are unfulfilled due to a lack of centralized IoT agricultural infrastructure. To address these issues, the article proposes a blockchain-based IoT model. This study also shows one-of-a-kind energy savings. The decentralization of data storage improves the supply chain's transparency and quality through blockchain technology, thus farmers can engage more efficiently. Blockchain technology improves supply chain traceability and security. This article provides a transparent, decentralized blockchain tracking solution and proposes an intelligent model protocol for several Internet of Things (IoT) devices that monitor crop development and the agricultural environment. A new approach has resolved the bulk of the supply chain difficulties. Smart contracts were utilized to organize all transactions in decentralized supply networks. The use of blockchain technology improves transaction quality, and customers may verify the legitimacy of an item's authenticity and legality by using the system. A total of 100 IoT nodes were distributed randomly to each 500 m2 cluster farm. The Internet of Things nodes were used to assess soil moisture, temperature, and crop disease. Network stability period and network life of the proposed method show 90.4% accuracy. The food supply chain will be more efficient and trustworthy with an intelligent model. The immutability of ledger technology and smart contract support further increases supply chain security, privacy, transparency, and trust among all stakeholders in the multi-party system. By 2050, the world's population will need a 70% increase in food production. The food supply chain will be more efficient and trustworthy with an intelligent model. This article provides a transparent, decentralized, and intelligent model protocol for several Internet of Things (IoT) devices.

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model.

In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. In this paper, these types of sentiments are classified to predict the mental illness of the user using the ensembled deep learning model. The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. In this work, multiclass classification is performed for predicting mental illness such as anxiety vs. nonanxiety, bipolar vs. nonbipolar, dementia vs. nondementia, and psychotic vs. nonpsychotic. The performance parameters used for evaluating the models are accuracy, precision, recall, and F1 score. The proposed ensemble model used for performing the multiclass classification has performed better than the other models, with an accuracy greater than 92% in predicting the class.